Method and apparatus for forming a pseudo-range model

ABSTRACT

A method and apparatus for locating mobile device over a broad coverage area using a wireless communications link that may have large and unknown latency. The apparatus comprises at least one mobile device, a reference network, a position server, a wireless carrier, and a location requester. The mobile device is in communication with the wireless carrier and receives global positioning system (GPS) signals from a plurality of satellites in the GPS satellite constellation. The reference network is coupled to the position server and provides GPS data. The mobile receiver receives GPS signals, performs rudimentary signal processing and transmits the processed signals to the wireless carrier. The wireless carrier passes the signals on to the position server. The position server processes the mobile receiver&#39;s GPS data and the reference network ephemeris data to identify the location of the mobile receiver. The location is sent to the location requester.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.09/990,479, filed Nov. 21, 2001 now U.S. Pat. No. 6,487,499, which is acontinuation of Ser No. 09/553,930 filed on Apr. 21, 2000 of U.S. Pat.No. 6,453,237, issued Sep. 17, 2002, which claims benefit of U.S.provisional patent application Ser. No. 60/130,882, filed Apr. 23, 1999,all of which are incorporated by reference herein in their entireties.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to mobile wireless devices as used inpersonal and asset location systems. In particular, the presentinvention relates to a method and apparatus for utilizing the GlobalPosition System (GPS) to locate objects over a large geographic regionand to provide information or services related to a real time positionof the object.

2. Description of the Related Art

With the advent of the Global Positioning System (GPS), there is growingdemand for mobile devices that can locate and track children, theelderly, emergency situations, tourists, security, valuable assets, andthe like. Devices built using conventional GPS receivers have beendeveloped by a number of companies. These current generation deviceshave major limitations in terms of indoor penetration, powerconsumption, accuracy, and acquisition time.

To address the above issues new GPS processing architectures haveevolved that utilize a combination of mobile GPS receivers and fixed GPSinfrastructure communicating via wireless links. Systems with thisarchitecture collect the majority of the data for location using thefixed infrastructure and, compared to traditional GPS, are able to offerlarge improvements in accuracy, indoor penetration, acquisition time,and power consumption. Thus far, such systems are based upon a fixedsite GPS receiver that is physically located in the local vicinity ofthe mobile receiver and are therefore difficult to extend to broadcoverage areas without a large proliferation of fixed site GPSreceivers. Furthermore, such systems require a wireless link thatprovides communication in real-time and therefore such systems cannottake advantage of non real-time messaging systems such as pagingnetworks.

Thus there is a need for a GPS processing architecture and devicetechnology that provides the benefits of improved accuracy, indoorpenetration, acquisition time, and power consumption and also offers thecapability to function over large geographic coverage areas withoutrequiring a fixed site GPS receiver in the local vicinity of the mobiledevice. Furthermore, to take advantage of broad coverage messagingsystems, the architecture should have the ability to operate over a linkwhich is not real-time, i.e. a link where there is significant andpossibly unknown message latency.

SUMMARY OF THE INVENTION

The invention provides a method and apparatus for locating a mobiledevice over a broad coverage area using a wireless communications linkthat may have large and unknown latency. The apparatus comprises atleast one mobile device containing global positioning system (GPS)processing elements, a GPS reference network comprising a plurality offixed site GPS receivers at known locations, a position server withsoftware that executes GPS processing algorithms, a wirelesscommunications link, and at least one location requester.

The method consists of using GPS measurements obtained at the fixed siteGPS receivers to build a real time model of the GPS constellation whichincludes models of satellite orbits, satellite clocks, and ionosphereand troposphere delays. The model is used by algorithms within theposition server to create an initialization packet used to initializeGPS processing elements in the mobile devices. Once initialized, the GPSprocessing elements detect and measure signals from the GPS satellites.The measurements made are returned to the position server, whereadditional software algorithms combine the information with thereal-time wide area model of the GPS constellation to solve for theposition of the mobile device. The computed position is then provided tothe location requestor.

The system design is such that messages to and from the mobile devicecan be delayed in time by an unknown amount as would be the case for anon real-time communication system. Furthermore, only a small number offixed site GPS receivers are required in the system and there is norequirement to have a fixed site GPS receiver in the local region of themobile device.

The GPS processing elements in the mobile devices include a highlyparallel GPS correlator that is capable of searching and detectingsignals over a wide range of unknown signal delays. The highly parallelnature of the GPS processing allows the system to use long averagingperiods which are impractical for a conventional GPS receiver thatsearches for signals sequentially using a small number of correlators.The long averaging times are made possible by the parallel correlationthat allow the system to locate objects in difficult signalenvironments, such as inside buildings, where conventional GPS cannotfunction.

Furthermore, the system design is such that the GPS processing elementsin the mobile devices are responsible for making only an instantaneousmeasurement of the sub-millisecond PN code phases of the receivedsignals, and do not collect GPS navigation data or time tag information.Conventional GPS receivers, by contrast, require a long time period(typically one minute or more) of continuous and strong signal receptionin order to acquire navigation data and timing. Thus, the systemproduces fixes much more quickly than a conventional GPS and can do soin environments where signals are relatively weak and unstable.

The overall architecture supports many different user models.Specifically, the location requestor may be the user of the mobiledevice or a different entity such as a Internet terminal or an emergencyassistance center.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentinvention are attained and can be understood in detail, a moreparticular description of the invention, briefly summarized above, maybe had by reference to the embodiments thereof which are illustrated inthe appended drawings.

It is to be noted, however, that the appended drawings illustrate onlytypical embodiments of this invention and are therefore not to beconsidered limiting of its scope, for the invention may admit to otherequally effective embodiments.

FIG. 1 depicts a block diagram of an object locating system;

FIGS. 2A and 2B together depict a flow diagram of a first embodiment ofa method for locating a mobile device;

FIG. 3 depicts a functional block diagram for using the wireless systemto adjusted for clock errors in a mobile device;

FIG. 4 depicts a flow diagram of a signal search method;

FIG. 5 depicts a flow diagram of a method of producing the correctinteger values of milliseconds in the pseudo-ranges;

FIG. 6 depicts a flow diagram of a software implementation of a parallelGPS correlator;

FIG. 7 depicts a flow diagram of a method of identifying truecorrelation peaks;

FIG. 8 depicts a functional block diagram of a hardware implementationof a parallel GPS correlator;

FIG. 9 depicts a flow diagram of a method for calculating position andtime from measured data;

FIG. 10 depicts a functional block diagram of a system for transferringlocation data in an emergency 911/cellular phone environment;

FIG. 11 depicts a functional block diagram of a system for transferringlocation data in a pager environment; and

FIG. 12 depicts a functional block diagram of a system for transferringlocation data in a wireless browser environment.

DETAILED DESCRIPTION OF THE INVENTION

To facilitate understanding the description has been organized asfollows:

Overview, introduces each of the components of the invention, anddescribes their relationship to one another;

Wide Area Model, describes the formation of the model of the GPSconstellation;

Initialization packet, describes how data from the wide area model isused by the mobile device to accelerate signal detection;

Convert PN code phases to full pseudo-range, describes how the fullpseudo-ranges are calculated at the server from the sub-millisecond codephases measured at the mobile device;

Compute position, describes how the server computes the mobile deviceposition, and time of measurement;

Fault detection, describes how the server identifies errors,particularly incorrect integer values in the pseudo-ranges;

Stored almanac model, describes an alternative method for providing theinitialization information for accelerated signal detection;

Compact orbit model, describes an alternative method for providing theinitialization information for accelerated signal detection. Thisalternative can be used by mobile devices that compute their ownposition;

Software implementation of the parallel GPS correlator, described oneembodiment of a parallel GPS correlator used for detection andmeasurements of GPS signals at a mobile device; and

Hardware implementation of the parallel GPS correlator, described asecond embodiment of a parallel GPS correlator used for detection andmeasurements of GPS signals at a mobile device.

Overview

The invention provides a method and apparatus for locating a mobiledevice over a broad coverage area using a wireless communications linkthat may have large and unknown latency. The apparatus comprises atleast one mobile device containing global positioning system (GPS)processing elements, a GPS reference network comprising a plurality offixed site GPS receivers at known locations, a position server withsoftware that executes GPS processing algorithms, a wirelesscommunications link, and at least one location requester.

The method consists of using GPS measurements obtained at the fixed siteGPS receivers to build a wide area model of the GPS constellation thatincludes real-time models for satellite orbits, satellite clocks, andionosphere and troposphere delays. The wide area model is used byalgorithms within the position server to create initializationinformation that is sent to the mobile devices. The GPS processingelements in the mobile devices use the initialization information,together with mobile clock information, to generate pseudo-range andpseudo-range rate predictions that allow the parallel GPS correlator torapidly detect and measure the PN code phase delays from a plurality ofsatellites. The PN code phase delays are returned to the positionserver, where additional software algorithms combine the informationwith the wide area model to solve for the position of the mobile device.It should be noted that solving for position requires only thesub-millisecond PN code phases from the mobile device, enabling themobile device to obtain the necessary measurement data much more quicklythan would be possible if full pseudo-ranges were needed.

The initialization information can take on several forms. In oneembodiment, the initialization information consists of models of thechanging pseudo range between the mobile device and each of a pluralityof satellites. The pseudo range models, after being suitably adjustedfor the effects of the clock in the mobile device, are used to generatepseudo range and pseudo range rate predictions for the parallel GPScorrelator. The models are useful over long time spans, to allow forlatency in receipt of the initialization packet by the mobile device.

In another embodiment, the initialization information consists of acompact orbit model that is used by the mobile device in a similarmanner to generate pseudo range and pseudo range rate predictions. Anadvantage of this approach is that the orbit model also carriesinformation sufficient for the mobile device to compute position withoutreturning measurements to the position server.

In another embodiment, the initialization information consists of deltacorrections that are used by the mobile device to correct pseudo rangeand pseudo range rate predictions that are computed from an almanacstored in the mobile device.

In all of the embodiments, the models and/or predictions of pseudo rangeand pseudo range rate are adjusted for the affect of the mobile deviceclock. The adjustment is created using tracking information in themobile device's wireless receiver which tracks a wireless carrier signalthat is itself synchronized to GPS.

FIG. 1 depicts a block diagram of a first embodiment of a personal andasset location system (PALS) 100. The PALS 100 uses a Global PositioningSystem (GPS) 101 (or other similar satellite position location system)having a plurality of satellites 102 orbiting the earth. PALS 100comprises a reference station network 115 comprising a plurality ofgeographically dispersed reference stations where each reference stationcomprises a fixed site GPS receivers 110 ₁ through 110 _(n)(collectively fixed site GPS receiver 110), a position server withsoftware that executes GPS processing algorithms 120 and a plurality ofmobile devices 130. The mobile devices 130 are coupled to or otherwiseassociated with an object that is to be located, e.g., mobile object 131including personal assets, equipment, persons and the like. The mobiledevices 130 communicate with the position server 120 via a wirelesscarrier 114. Each reference station 110 further comprises a conventionalGPS receiver 112 ₁ through 112 _(n) (collectively conventional GPSreceivers 112) located at a precisely known locations. For example, fora global network, the network may comprise just a few stations toobserve all satellites at all times, with more stations added to furtherimprove the model of the GPS constellation. Each of the conventional GPSreceivers 112 is coupled to the position server 120 via a networkcommunications link 103.

In one embodiment, the position server 120 is utilized to determine thelocation of the mobile receiver 130. The mobile device 130 contains awireless communications transceiver 140 that enables the receiver tocommunicate with the position server 120 through the wireless carrier114. One embodiment of the invention uses a duplex wireless protocol tocommunicate with the mobile device 130 via links 107 and 109 (anapplication of the invention is further described with reference to FIG.8 below). The wireless carrier communicates with the server through aconventional communication network 111.

As discussed below, the device 130 comprises a wireless transceiver 140,a GPS receiver front end 134, and a GPS signal processor 138. The GPSsignal processor 138 includes a highly parallel GPS correlator andassociated software to perform various algorithms described below. Themobile device 130 receives initialization data from the position server120 through the wireless link 109, collects certain GPS signalinformation, processes that information and sends the processedinformation through link 107 to the wireless carrier 114. The wirelesscarrier 114 transmits the information through link 111 to the positionserver 120. In one embodiment, the position server 120 processes the GPSinformation from the device 130 to determine the device's location. Alocation requestor 122 can then request the receiver's location througha number of communications paths 105, e.g., dial up access, Internetaccess, wired land line and the like. The location requestor can also bethe user of the mobile device in which case location requests could alsobe communicated through the wireless carrier.

The conventional fixed site GPS receivers 112 of the reference stationnetwork 115 transmit GPS measurements received from all the visiblesatellites 102. The data is transmitted from each conventional GPSreceiver 112 to the position server 120. For example, the data may betransmitted through the reference station network 115 via a router anddedicated landline (e.g., ISDN, T1, T2, and the like) or in TCP/IPformat over the Internet to a hub at the position server 120. Thecommunication network components are represented by links 103.Thereafter, the position server 120 is responsible for computing theposition of the mobile device 130 by using in part, the GPS datatransmitted across the reference station network 115.

Wide Area Model

In order to determine the position of the mobile device 130, the PALS100 utilizes a wide area inverse differential GPS technique to locatethe mobile device 130. Specifically, the position server 120 uses thereference network 115 information to build a real-time wide area modelof the GPS constellation which includes estimates of satellite orbits,satellite clocks, ionosphere and troposphere delays. The wide area modelis used for two purposes. First, the model is used to generate aninitialization packet that is transmitted to the mobile device and isused by the GPS signal processor to help detect and measure the GPSsatellite signals. Second, the model is used together with the PN codephase values from the mobile device 130 to solve for the position of themobile device 130.

FIG. 2 depicts the proper alignment of FIGS. 2A and 2B. FIGS. 2A and 2B,taken together, depict a flow diagram of a first embodiment of a PALSprocessing method 200 for locating a mobile device. In step 205, the GPSmeasurements from GPS receivers 112 of the reference station network 115are sent to the position server 120. At step 210, the position server120 combines a plurality of GPS satellite 112 measurements to produce awide area model 220 that is used to generate real-time estimates ofparameters related to the GPS system including satellite orbits 221,satellite clocks 222, and a geometric model of the atmospheric delay223. The atmospheric delay models 223 are useful within the geographicregion spanned by the reference network 115. The orbit and satellitemodeling information 221, 222 is provided for all satellites and isuseful for any mobile device 130. In one embodiment of the invention,the parameters are estimated by a Kalman filter that iterativelyconverges upon a large number of parameters that form a least squaresfit to the data observed at each reference station 110. These wide areamodeling techniques are well known in the industry. When used in thisembodiment, these wide area modeling techniques provide accuracy and theability to predict satellite signals over a broad coverage region usinga relatively small number of receivers 112 in the reference network 115.Thus, one advantage of utilizing the wide area differential GPStechnique is that the reference stations 110 in the network 115 can bespaced further apart than would be required with conventional DGPS. Thismeans that, to achieve an accurate position for the mobile device, nofixed site GPS receiver is required in the vicinity of the mobiledevice.

Additionally, since the creation and maintenance of the model occursonce and is usable for all mobile device positions, the technique iscomputationally efficient when large number of mobile devices 130 mustbe serviced concurrently.

Initialization Packet

To determine the position of an object 131, a location request isreceived at step 245 for the position of a mobile device 130. At step250, the wireless carrier 114, in turn, sends a request to the positionserver 120 requesting initialization information for the GPS processingin the mobile device 130. This request is accompanied by a roughestimate of position 250 for the mobile device 130.

The approximate position of the mobile device 130 is provided by thewireless carrier 114 to the position server 120 through a conventionallink 111. In a cellular phone system, for example, this information canbe derived from knowledge of the particular cellular base station beingused to communicate with the mobile device 130. Similarly, in a 2-waypaging system, the registration of the pager into a market service areaprovides the wireless carrier 114 with a rough idea of the position ofthe mobile device.

The position server makes use of the rough estimate of position 250 andthe wide area model 220 to create an initialization packet that istransmitted to the mobile device. The mobile device uses theinitialization packet to calculate the expected satellite ranges andrange rates. These will be used to drive the parallel GPS correlator inorder to accelerate signal detection and measurement.

The range and range rates of a satellite signal, as measured by themobile device, are also affected by time and frequency referencingerrors in the mobile device. Conventional GPS receivers address thisuncertainty either by searching over a large range of possiblefrequencies and possible code-delays, or by tuning or steering themobile device oscillator with an accurate external reference. Asdescribed below, the invention uses a novel technique to adjust theinformation in the initialization packet for the mobile clock error,avoiding the need for a large search or a steered/tuned oscillator.

One embodiment of the initialization packet is a pseudo-range model,described in FIG. 2A. To create the pseudo-range model, the server firstcreates a geometric-range model at step 260. In one embodiment this isdone by taking the norm of the vector from the rough estimate of mobiledevice position to the real-time estimate of satellite position 221supplied by the wide area model.range_(l)=|satellite_position_(i)−mobile_device_position|  (1)

To create the pseudo-range model, at step 265, the position server 120adjusts the geometric-range model to account for the effect of thesatellite clock error 222 and the ionosphere/troposphere correction. Toinclude the effect of the satellite clock error the range is adjusted byan amount equal to the satellite clock error multiplied by the speed oflight. To include the effect of the ionosphere/troposphere error, therange is further adjusted by an amount equal to theionosphere/troposphere delay, in the vicinity of the mobile device,multiplied by the speed of light.

The pseudo-range model thus created is valid at a specific time. In oneembodiment, to make the model valid over a longer period of time, theserver calculates a similar model at several different times and thendoes a polynomial fit to the data to create a model parameterized inthree terms, a, b, and c, for each satellite. A valid model can beobtained at some later time (dt) as follows:pseudo_range_(l)(t+dt)=a _(i) +b _(i) .dt+c _(i) .dt ²  (2)

This allows the method to be used with systems that have large andunknown latency in the communication link, so that the mobile devicewill need to apply the model as some later time.

It is understood that there are many other mathematical techniques thatcan be employed. In general, the number of terms and precision requiredin the delay model will be a function of the desired accuracy for themodel as well as the time span over which the model will be used. Forexample, a third order polynomial fit is accurate enough to providemodels of the delay that are sufficiently accurate to assist the GPSsignal processing in the mobile device over periods of several minutes.

The pseudo-range model provides for estimating the delay of a signalfrom the satellite and the changes in delay over time, allowing the GPSsignal processing to generate both time and frequency predictions forthe GPS satellite signal at the mobile device 130.

The pseudo-range model provides the mobile device with two separablepieces of information. The pseudo-range value tells the mobile devicewhat code phase to expect from each satellite, and the rate of change ofpseudo-range tell the rate of change of the code- and carrier phase. Insome systems it may be desirable to take advantage of the rate of changeinformation only. In this case the term a_(i) may be ignored from themodel in equation (2).

Referring to FIG. 2A, the pseudo-range models are next adjusted for theclock in the mobile device. One embodiment of the adjusted model is anadjusted pseudo-range model, formed as follows: a model A, B, C isformed for the mobile device clock errorclock_error(t+dt)=A+B.dt+C.dt ²  (3)

At step 270, this model is then incorporated into the initialpseudo-range model that is produced in step 265 to form the adjustedpseudo-range model. The adjusted pseudo-range model takes the form:pseudo_range_(l)(d+dt)=(A.c+a _(i))+(B.c+b _(i)).dt+(C.c+c _(l)).dt²  (4)where c is the speed of light.

To determine the additional coefficients A, B, C the GPS processingtakes advantage of the fact that in most wireless systems, the wirelesstransmission themselves are synchronized to some degree with the GPSsystem. For example, modern paging systems use a time domain multiplexedprotocol wherein transmissions from individual transmitters in thesystem are all time synchronized with GPS.

The synchronism of the wireless system can take on several forms. Insome systems, for example older analog cellular phones, only thefrequencies of transmissions may be controlled, and while the timing oftransmission is arbitrary. In other systems, such as pagers, thefrequencies of transmissions may be somewhat arbitrary, but the timingof transmissions is closely controlled to support time domainmultiplexing of the channel. Finally, in emerging wireless standards,such as those using code domain multiple access, both frequency andtiming of transmissions are closely controlled. For the purpose of thefollowing description, it is assumed that the wireless system has bothfrequency and timing synchronization with the GPS system.

FIG. 3. illustrates a method for using the wireless system 300 to createthe adjusted pseudo-range models. A wireless transceiver contains asoftware demodulator 301, generally implemented in a high speed digitalsignal processor (DSP). The software demodulator 301, among other tasks,is responsible for tracking the wireless carrier and determining clockand frame timing of data modulation on the wireless carrier. Whenlocked, certain values within the tracking loops of the demodulator willbe representative of the offset of the mobile device time and frequencyreference from that of the wireless carrier and the data modulation.Since the latter are approximately synchronized with GPS, it may be seenthat the values within the tracking loops are representative of theoffsets between the time and frequency reference in the mobile devicefrom the GPS system.

In particular, in reference to the equation (3) above, the value A isderived from value of time offset between a reference clock in themobile device and a frame synchronization point in the wireless carriermodulation. The term B above is derived from the frequency term from thecarrier tracking loop in the software demodulator. The term C above is aclock acceleration term that may also be derived from a second orderfilter term within the carrier tracking loop. At step 270, appropriatelyderived values for A, B, and C are applied to the pseudo-range modelfrom step 265 to create the adjusted pseudo-range model.

In some systems, the mobile device may contain a feedback loop thatkeeps the frequency reference for the wireless receiver in theapproximate range of the carrier signal. This is no way detracts fromthe method described since at any moment in time the tracking values inthe software demodulator are representative of the instantaneous offsetbetween the frequency reference for the mobile device and the wirelesscarrier.

Furthermore, in some systems it may be desirable to use separateoscillators or time references to control the processing in the wirelessreceiver and the GPS. In this case, a sync signal 311 may be optionallyprovided from the wireless receiver to the GPS processing. The GPSprocessing samples and tracks the sync signal to determine, on a realtime basis, the offset between the separate oscillators and/or timereferences using a sync tracking circuit 312. These offsets, whencombined with the tracking values from the wireless receiver, representthe total offset of the GPS time and frequency reference from thewireless carrier. The sync tracking circuit 312, for example, couldconsist of a numerically controlled oscillator (NCO) in the GPS that isdriven by a feedback loop to generate a pulse in synchronism with thesync signal 311. In this case, the phase value in the NCO at someinternal time epoch within the GPS, would be representative of the delaybetween that epoch and the sync signal 311. Furthermore, the frequencyvalue in the NCO would be representative of the relative rate betweenclock signals in the GPS and the clock signal in the wireless deviceused to generate the sync signal 311. In step 313, these values areappropriately scaled and added to the tracking values from the softwaredemodulator 301 to adjust for the separate GPS clock. In step 270, thesemodified values are used instead of the unmodified outputs from thesoftware demodulator to form the adjusted pseudo-range model.

The above discussion assumed that the wireless carrier is synchronizedin time and frequency to the GPS constellation. In the case where thewireless carrier has only frequency synchronization, it is not possibleto compute the A term in equation (3). In this case, the adjustedpseudo-range model can still be created but will be initiallyuncorrected for an arbitrary bias in the clock term (A.c+a_(i)). Themodel is nevertheless useful because it contains information aboutpseudo-range rates. Furthermore, since the unknown term A is identicalfor all satellites, the GPS processing can subsequently solve for theterm A once the pseudo-range delay to a single satellite is determined.

It should be noted that the PALS method does not require or assume thatthe techniques described above provide perfect time and frequencysynchronization with the GPS system. In practice, there are many sourcesof possible error in the coefficients A,B,C including errors in thetiming and frequency of the wireless transmissions, tracking errors inthe software demodulator, and unknown delays and frequency deviationscaused by the motion of the mobile device. As will be discussed below,the adjusted pseudo-range models are used to predict only nominal valuesof pseudo-range and pseudo-range rate to which uncertainty bands areadded to ensure that the range of signal search is adequate.

Returning to FIG. 2, the adjusted pseudo-range models generated at step270 assist in the signal search function of step 290 that is performedby the GPS signal processing in the mobile receiver 130. Specifically,the adjusted pseudo-range model produces bounds on the uncertaintiesassociated with the expected frequency and time of arrival of thesatellite signals. This enables the signal search function to accuratelyguide the detection and measurement process that is performed at step291. It should be noted that steps 291 and 290 are interactive in thatthe results of detection and measurement can further guide the signalsearch step (as represented by path 285). Details of the signal searchmethod employed in the preferred embodiment are described below withrespect to FIG. 4.

It is understood that the pseudo-range model is just one embodiment ofthe invention's use of the initialization packet for accelerated signaldetection and measurement. Two other embodiments, stored almanac model &compact orbit model, are described later.

FIG. 4 is a flow diagram of a method 400 of signal search (step 290).The method begins at step 402 with an input of adjusted pseudo rangemodel of step 270. At step 404, the model is applied at the current timein the mobile device and is used to estimate the current frequency andtiming of GPS satellite signals, as well as the expected uncertaintiesof these quantities, to form a frequency and delay search window foreach satellite. This window is centered on the best estimates offrequency and delay but allows for actual variations from the bestestimates due to errors in the modeling process including inaccuraciesin the rough user position, errors in the time and frequency transferfrom the wireless carrier etc. In addition, the frequency uncertainty isdivided into a number of frequency search bins to cover the frequencysearch window.

In step 406, the detection and measurement process of step 291 in FIG. 2is then set to program the carrier correction to the first searchfrequency. At step 408, the parallel correlator is invoked to search forsignal correlations within the delay range of the delay window. At step410, the method 400 queries whether a signal is detected. If no signalis detected, the carrier correction is set, at step 412, to the nextsearch frequency and the search continues until a signal is found or thefrequency search bins are exhausted.

If, at step 410, the method 400 affirmatively answers the query, thesignal is used at step 414 to further improve the estimate of clock timedelay and clock frequency offset. This information is utilized at step416 to re-compute the frequency and delay search windows for theremaining undetected satellites. In step 418, the process continuesuntil all satellites have been detected or the search windows have beenexhausted.

The method of FIG. 4 is illustrative of one of a variety of algorithmsthat can be used to guide the search process based on the GPS signalprocessing's ability to estimate time and frequency. Additionally, thealgorithms could be altered to include various retry mechanisms sincethe signals themselves may be fading or blocked.

Convert PN Code Phases to Full Pseudo-Range

The output of the detection and measurement process (step 291) is a setof sub-millisecond PN code phase values 292 for as many satellites ascould be detected by the GPS baseband processor. This information issent to the position server 120 through links 109 and 111.

In order to convert the values to full pseudo-ranges at step 295, it isnecessary to ascertain the number of complete PN cycles (integer number)that must be added to the PN code phase value 292 to reach a fullpseudo-range. FIG. 5 depicts a flow diagram of a method 500 forascertaining the complete PN cycles.

In the method, only the relative integer values, not the actual values,are required, that is, if all the integer values are adjusted by thesame amount, the subsequent position and time solution produced at step296 will be the same. This is because the position and time solutionwill remove any common-mode error. It is noted that all pseudo-rangesare expressed in milliseconds to represent the time of flight of the GPSsignal from the satellite to the mobile device.

The method 500 begins with the rough estimate of user position from step250 and rough estimate of the time of arrival of the GPS signal at themobile device (step 502), obtained from the real time clock at theposition server. In one embodiment, a reference satellite is chosen asthe satellite with the highest elevation angle of all the availablesatellites. The PN integer number for this satellite is estimated byrounding the difference between the pseudo-range model (expressed inmilliseconds) and the measured sub-millisecond PN code phase (expressedin milliseconds):N ₁=round(pseudo_range₁ −PN_code_phase₁)  (5)

The full pseudo-range is computed at step 504 for the referencesatellite is then computed by adding the computed integer to thesub-millisecond PN code phase at step 501. At step 505, the method 500computes the PN integer numbers for the other satellites by rounding thedifference of the full pseudo-range of the reference satellite with thesub-millisecond PN code phase of each of the other satellites.N _(i)=round(pseudo_range₁ −PN_code_phase_(i))  (6)

Note that this equation (6) differs in a subtle but important way fromequation (5), in that the pseudo-range used is for satellite 1, whileother terms are for the satellite i, resulting in a total cancellationof all common mode errors in the measurements (most of these come fromthe mobile device clock error).

It is understood that other techniques may be used to estimate theintegers, some of which may not difference between satellites as doneabove.

Because the satellite range (expressed in milliseconds) changes by lessthan 0.17 milliseconds per minute, the above technique will typicallyyield the correct integers whenever the rough time estimate is withintwo or three minutes of the correct GPS time of measurement. Also,because one millisecond of range corresponds to approximately 300 km indistance, this technique typically yields the correct integers when therough user estimate is within approximately 100 km of the true positionof the mobile device. When operating over a wide area such that theestimate of user position has a larger error, or the latency is severalminutes, there will be multiple sets of possible integers. To ensurethat the correct integers are selected, the method 500, at step 506,computes all sets of possible integers. For each set, the serverperforms a position computation. If the solution is over-determined, aset of residual values is obtained, indicating the degree of fitachieved in the least squares algorithm. Incorrect integers will yieldvery large residuals, and they can be eliminated, leaving only thecorrect combination of possible integers and the correct position andtime.

Returning to FIG. 2, the ability of the residual detection method toidentify the correct set of integers is further enhanced by using rangemeasurements from other sources, such as altitude estimates from aterrain model, time of arrival measurements from wireless communicationslinks, angle of arrival measurements at cell towers, and the like. Eachof these class of measurements may be also included in the positioncomputation 296 as described in FIG. 9.

Compute Position

FIG. 9 depicts a flow diagram of a method 900 for computing the mobiledevice position and time of GPS measurement. The method requires noknowledge of time of measurement from the mobile device, in that onlysub-millisecond PN code phase information is supplied from the mobiledevice, and all other information is obtained from the network of GPSreference stations 110, or computed at the server as described below.

All sets of possible integers are computed in step 506 of FIG. 5. Foreach of the possible integer combinations, the a-priori range residualsare calculated by differencing the measured pseudo-ranges from thepseudo-range model from step 265 of FIG. 2A. The pseudo-range modelrelies on the calculation of satellite positions at the time estimatedby the server. The error in this estimate is unknown, because thelatency of the communication link is unknown, but it can be accuratelycalculated in step 902, after using an initial latency estimate of zeroseconds, and iteratively updating this estimate with the result of step902. The model of latency error is incorporated in the positionequation. One embodiment of the position equation is:y=Hx  (7)where:

-   -   y is the vector of a-priori range residuals 901;    -   x is the vector of: updates to user position, the common mode        errors, and the latency error; and    -   The updates to user position are commonly, but not necessarily,        expressed in the coordinates: East, North, Up.        H is a matrix with five columns. The first three columns are        line-of-sight vectors, of unit length, pointing from the        satellites to the rough user position. The fourth column is all        ones, this is the model of the effect of the common mode errors        on the measurements. The fifth column contains the negative of        the range rate, which is the model of the effect of latency on        the measurements.

Note that the first four columns of H and the first four elements of thevector x are standard in the GPS literature. The innovation in thismethod is the inclusion of an exact model of the latency error. Notefurther that the latency error is not a common mode error, like themobile device clock error (which is present in all the sub-millisecondPN code phases). The latency error affects each of the ranges in adifferent way, since each of the satellites has a different range rate.However, because this method exactly models the effect of latency in theposition equation the error is completely removed in the solution of theequation. Thus, there is no need to get any information from the mobiledevice other than the sub-millisecond PN code phases. In particular, notime tag is required.

Because the above equation solves for the latency error, at the sametime as solving for the mobile device position, the method can be usedin systems with large and unknown latency in the communication link.

There are many standard ways to solve the position equation, describedin standard linear algebra texts. One embodiment is:x=(H ^(T) H)⁻¹ H ^(T) y  (8)

Once the position equation has been solved, the user position, andserver time can be updated at step 903. The pseudo-range model is thenrecalculated with a more accurate estimate of the satellite positionsand the mobile device position and time of measurement (with latencycorrected). This iteration is repeated until the solution converges.

Returning to FIG. 2, it should be noted that the method 200 has theproperty that it implicitly corrects the errors typical in GPS, and doesso in a way that is significantly different from conventional GPStechniques.

Conventionally GPS errors are corrected by a technique known asDifferential GPS (DGPS), in which a GPS reference station is located inthe vicinity of the mobile device. GPS errors measured at the mobiledevice will also be measured at the reference station. The referencestation, being located at a known point, can calculate the effect of theGPS errors, and provide a means for correcting these errors in themobile device, or in the data transmitted from the mobile device. Thereference station does not need to, and typically does not, calculatethe component parts of the GPS errors. The technique relies on the factthat the cumulative effect of the GPS errors is similar at the referencestation and at the mobile device. Implicit in this is the requirementthat the reference station be close to the mobile device.

The data from the mobile device may be transmitted to the referencestation and processed there. In this case the technique is known asInverse DGPS. The same constraint, that the reference station be closeto the mobile device, applies.

The current invention provides for a Wide Area Inverse Differential GPStechnique, with the significant innovation that the corrections to thestandard GPS errors are implicit in the method of calculating position,and applicable to any mobile device anywhere in the world. This isbecause, in the method 200, the position computation being performed atthe server, uses a wide area model 220 that is already precise, that is,the GPS errors that would usually afflict a standard GPS system havealready been removed. Thus the computed position is not subject to thestandard GPS errors, no matter where the mobile device is located.

In step 298, extra measurements, from external sources or models, can beincluded in the position equation as follows. For each extra measurementan equation is formed relating the measurement to the states in thevector x. One embodiment of an extra measurement, that is alwaysavailable, is the use of a terrain model to estimate the height of themobile device. A terrain model may be stored in a database accessible bythe server. Using the estimated position of the mobile device, the modelis used to derive a measure of the device's altitude. This is then addedas an extra row to the position equation (7):y _(altitude)=H_(altitude) .x  (9)where:

-   -   y_(altitude) is the measurement residual associated with the        rough user position, and the altitude model,    -   y_(altitude)=altitude_model−rough_user_altitude    -   H_(altitude) is the row added to the H matrix to describe the        relationship between x and y_(altitude),        H_(altitude)=[0,0,1,0,0]    -   x is as described above in equation (7), with the updates to the        rough user position expressed in coordinates East, North, Up.

Another embodiment of extra measurements is the use of time-of-arrivalmeasurements that may be available from the communications-link used tosend data to or from the mobile device. These measurements give ameasure of the distance of the mobile device to a fixed point. Thesemeasurements can be included in the position equation (7) in a similarway to the satellite pseudo-range measurements. An extra row is added tothe position equation for each extra measurement, and the elements ofthe matrix H are used to model the relationship between the states, x,and the measurement residuals, y.

Another embodiment of extra measurements is the use of angle-of-arrivalinformation available from wireless systems with directional antennas.These measurements can be added into the position equation in a similarway to that described above.

Another embodiment of extra measurements is from other satellite systemsfrom which range measurements may be available. These measurements areincluded in a similar way to the GPS measurements described above.

It is understood that other standard mathematical techniques may be usedto include extra measurement information, for example, the techniquedescribed above for including altitude as an extra line in the positionequation (7) may equivalently be done by removing one of the unknownstates in the same position equation.

One reason for using extra measurement information is that the positionequation (7) typically requires at least as many measurements as unknownstates in order to solve the equation for the unknown states. The moremeasurements that are available, the better the system will work. Inparticular, a system that uses measurements from sources other than GPSwill be able to calculate a position in low signal strengthenvironments, such as indoors, where it may be difficult or impossibleto make measurements from multiple GPS satellites.

Another reason for using extra measurement information is that itenhances the fault detection methods described below.

Fault Detection

Once a position has been computed at step 296, a process known as faultdetection is used at step 297 to determine whether there are significanterrors in the data used to obtain the position. There are many faultdetection techniques, described in the GPS literature, that areapplicable to the current invention.

One example of a fault detection technique is the use of an overdetermined position equation (7) to form post-fit residuals. Anover-determined equation is one with more measurements than unknownstates. The post-fit residuals are the differences between the actualmeasurements and the measurements that are expected given the calculatedstates (in the example above the states are: updated position of themobile device, common mode errors and the latency). For anover-determined solution the magnitude of the residuals will be of thesame order as the magnitude of errors in the measurements. Thus, byexamining the magnitude of the residuals, the system can tell if therewere any significant measurement errors. This technique is especiallyuseful in the context of the invention, where errors may be introduceddue to the incorrect integers being used in the pseudo-ranges. If thecorrect integers are used, the post-fit residuals will be of the orderof several meters, while if the incorrect integers are used then thepost-fit residuals will be of the same order as the incorrectpseudo-ranges, which is hundreds of kilometers, because each integernumber of milliseconds corresponds to almost 300 km of pseudo-rangeerror. Thus the method can readily determine which position solutioncorresponds to the correct integers. This, in part, explains why theinvention provides a wide area solution, where the approximate mobiledevice position may be very poorly known, and the time of measurement ofthe signals may not be known at all. As described earlier, all possibleintegers can be considered, and the server can eliminate incorrecterrors through the fault detection technique. Because this faultdetection technique relies on an over-determined solution, theperformance of this method is enhanced by the addition of extrameasurements from sources other than GPS.

Another example of an applicable fault detection technique is to checkthe position and/or time solution against known constraints on theposition and/or time. For example, if the altitude of the mobile deviceis known, then a reasonableness check can be done on any computedposition to see if the computed altitude agrees, within some bounds,with the known altitude. Similarly any other known constraint onposition and/or time may be used as a reasonableness check.

This technique can be used in addition to the post-fit residualtechnique described above.

The fault detection techniques are also employed to guard against faultyposition results caused by incorrect measurements from the GPSprocessing in the mobile device. The fault detection, for example, candetect an erroneous reading caused by the misidentification of acorrelation peak, or by the receipt of a signal with large multipathdelay. The result, at step 299, is an accurate position for the mobiledevice.

Stored Almanac Model

In FIG. 2A, an alternative method is shown to the pseudo-range modeldescribed above. At step 280, this method uses a stored almanac model toprovide initialization information to accelerate signal detection in themobile device. This alternate embodiment stores a GPS satellite almanacin the GPS processing in the mobile device. The GPS satellite almanac isa compact model of the satellite orbits and clocks, broadcast by the GPSsatellites, and intended primarily for use in selecting satellites inview. In this embodiment the position server sends the rough userposition at step 250 and a server time estimate to the GPS processing inthe mobile device. The latter uses the almanac models, together withtime and position, to generate pseudo-range models using processingalgorithms similar to those described in steps 260 and 265. The resultof this processing is a pseudo-range model that will differ slightlyfrom that created in steps 260 and 265 by the position server, thedifferences arising from the deviation between the almanac model oforbit and clocks and the precise models of orbits and clocks availablefrom step 221 and 222.

The position server concurrently maintains a copy of the almanac thatexists in the mobile GPS processing. The position server computes apseudo-range model based on this almanac (mirroring the computation inthe mobile device) and compares the result to the precise pseudo-rangemodel of step 265. Information representing the differences between themodels is then transmitted to the mobile device, allowing the mobile GPSprocessing to improve upon the pseudo-range model that was initiallycomputed from the stored almanac.

For example, in one embodiment, the correction terms sent by the serverconsist of delta pseudo-range rates that allow the mobile device toimprove upon the pseudo-range rate term in its model. Often it will beimportant to correct this term since pseudo-range rate information isused to guide the parallel GPS correlator (see below).

Furthermore it is understood that the adjustments shown in FIG. 3 andits description above are necessary for this alternate embodiment inorder to adjust the information calculated from the almanac for theeffect of the mobile receiver clock.

Compact Orbit Model

In FIG. 2A by using step 290, an alternative method to the pseudo-rangemodel described above is formed. This method produces a compact orbitmodel. The embodiment is useful when it is desired to provide a set ofcompact composite orbit models to the GPS processor 138 in the mobiledevice 130 rather than providing the pseudo-range model. Two reasons forproviding a compact composite orbit model are: First, a single compactcomposite orbit model could be broadcast for use by any number of mobiledevices in a large region. Second, the availability of the compactcomposite model enables the mobile device to calculate its own positionon an autonomous basis without further interaction from the server.

The method for computing the model involves taking the wide area model220, which is valid worldwide and over a large period of time, andreducing it to a more compact model that is valid over a specificgeographic area for a specific time window. The reason for doing this,instead of simply broadcasting the model 220, is that the compact modelcan be packed into a smaller data structure.

One embodiment of the alternative method is to compute satellitepositions (using model 220) at several times t₁ through t_(n). Apolynomial curve fit is then done. The parameters of this curve fit thenmake up a compact orbit model.

Another embodiment is to absorb the clock errors andtroposphere/ionosphere errors into the orbit model by computing anequivalent orbit that will yield the same pseudo-range as the originalorbit model adjusted by clock, ionosphere and troposphere corrections.

It is understood that there are other similar mathematical techniques tocreate similar or identical compact models that are valid over someregion, and over some time window.

Furthermore it is understood that the adjustments shown in FIG. 3 andits description above are necessary for this alternate embodiment inorder to adjust the information calculated from the compact model forthe effect of the mobile receiver clock.

Software Embodiment of the Parallel GPS Correlator

FIG. 6 is a flow diagram of a method 600 for performing the GPS signalprocessing in the mobile device. This embodiment uses a digital signalprocessor (DSP) operating on stored input. In one embodiment of theinvention, the method is implemented as a software routine as describedbelow. To capture the necessary GPS signal, input samples are receivedby the mobile device via a conventional GPS front end which translatesthe input signals to an IF frequency. Digital samples are taken usingeither a multi-bit ADC or a 1 bit binary comparator. At step 610, thecaptured samples are then stored in memory within the mobile device forsubsequent processing. Typically, several hundred milliseconds of dataare stored.

The method 600 consists of two major processes; a signal detectionprocess 601 and a signal measurement process 602. The signal detectionprocess 601 determines the presence or absence of a GPS signal and theapproximate PN code delay for the signal. Then, in the signalmeasurement process 602, the precise value of the PN code phase isdetermined.

The signal detection process 601 consists of several steps as outlinedbelow. At step 611, the first phase involves applying a carrierfrequency correction term as provided by the signal search step 406 or412. To apply the correction, the input samples are multiplied by acomplex exponential term equal to the complex conjugate of the carrierfrequency correction. By adjusting the correction term, the nominal IFtuning offset inherent in the RF front end design can also be removedduring this step. The output of step 611 yields a complex result, i.e.the result is composed of an in-phase term (generated by multiplyingwith the cosine function of the carrier frequency) and a quadrature term(generated by multiplying with a sine function of the carrierfrequency). For simplicity, these complex quantities are not explicitlyillustrated in FIG. 6.

At step 612, the input samples are pre-summed prior to processing toimprove SNR and to reduce the processing burden. The pre-sum operationtakes advantage of the fact that GPS signals consist of at least twentyidentical epochs (each epoch consisting of a full cycle of the PN codeand twenty epochs being the data bit period). Samples taken at the samerelative position within small groups of succeeding epochs can be summedto yield a single set of samples representative of all epochs. In oneembodiment, the pre-summing operation is performed over groups of nineepochs, a value which ensures that data bit transitions on the GPScarrier will usually not affect the pre-summed quantities. By contrast,pre-summing over longer periods would tend towards zero due to the databit transitions.

A convolution operation (multi-step process 620) is then performed toidentify points of correlation between the input signal and the knownsatellite signal. While this convolution can be performed by a varietyof techniques, an FFT based approach, commonly known as a fastconvolution, is computationally efficient.

More specifically, the fast convolution process 620 begins by performingan FFT at step 621 on the block of input samples. At step 623, theresult is multiplied by the FFT of the PN code waveform 622. Then, atstep 624, the method 600 multiplies the product by a time driftcorrection 626. At step 625 an inverse FFT of the result is computed toobtain the desired convolution. To save computational load, the FFT ofthe PN code for all satellites is pre-computed and stored in memory.

To improve SNR, the results of many fast convolutions are summed in anon coherent integration step 630 by summing the magnitude square of theindividual convolutions. The result is an improved SNR magnitude squaredestimate of the convolution. The non coherent integration step 630requires that the individual convolutions be time aligned to account forthe drift of the PN code between the pre-summed groups. The expectedtime drift between each pre-sum group may be computed because theexpected code frequency is known for the search (the code frequency willalways be {fraction (1/1540)} of the carrier frequency). The time driftis conveniently compensated for during the convolution operation byapplying a time drift correction 626 during step 624. In step 624, thetransform domain representation of the convolution is multiplied by acomplex exponential with a linear phase characteristic, which, it may beunderstood, has the effect of shifting the convolution output in time.As each group is processed, the slope of the linear phase term in step626 is increased to compensate for the expected time shift of the PNcode relative to the first group. Thus in this manner, all theconvolution outputs will be approximately aligned in time and may besummed.

The non coherent integration is followed by step 640, wherein a peakdetection is performed in which the results of the non coherentintegration are scanned for correlation peaks. The resulting list ofpeaks are further analyzed during peak identification at step 650. Thelist is stripped of false peaks that may result from correlationsidelobes. FIG. 7 and the following description provide a detailed flowdiagram for the peak identification process of step 650, the result ofwhich is an identified peak location for the each satellite.

It should be noted that the fast convolution and peak identificationtechnique of the software method 600 is intended only to identify theapproximate delay value for the satellite, e.g. the approximate peaklocation. To obtain better accuracy the method proceeds to the signalmeasurement process. This process makes a precise measurement of thedelay value for the satellite, e.g. the exact peak location isdetermined.

The signal measurement process 602 begins with the original stored IFsamples, then, at step 661, proceeds with a carrier correction step thatis methodically identical to 611. The output of the carrier correctionstep is coupled to an early-late (E-L) correlator 660. The carriercorrected IF samples are multiplied by both early and late versions ofthe PN reference code generated by the PN generator of step 661. Theearly and late products are differenced to form an early minus latesignal that is accumulated for samples spanning several epochs. Thecomplex magnitude squared value of the accumulator output is formed atstep 673, and these values are further accumulated over a longer timespan in the non coherent accumulator at step 675. The result is a wellaveraged value of the E-L correlation.

The accumulations leading to the E-L output consist of both coherentsummation and non coherent (magnitude square) summation. In oneembodiment, the coherent summation interval is chosen as nine epochs.This value is short enough to ensures that data bit transitions due tothe GPS navigation message will not cause significant loss when averaged(see discussion above). Furthermore, limiting the coherent averagingtime relaxes the requirement that the carrier correction process behighly accurate.

The PN generator of step 661 produces the reference code used in theearly-late correlator 660. Initially, the code offset, e.g. the startingposition of the code relative to the stored input samples, is set to thevalue resulting from peak identification process of step 650. At step663, the rate of code generation is set by the code numericallycontrolled oscillator (code NCO) to the expected code rate as determinedfrom the adjusted pseudo-range model of step 270 in FIG. 2.

The averaged value of the E-L correlation is used to update the phase ofthe NCO, in order to achieve better alignment the PN code generator tothe input signal. When the best alignment is achieved, the E-Lcorrelator output value will be minimized. This phase updating of theNCO continues in an iterative fashion until the E-L correlator outputvalue reaches an acceptably small level (i.e., the threshold value atstep 680). Once reached, the delay estimate produced at step 665 is aconsidered the final value of the PN code phase that is output at step690.

A variation of the method eliminates the iterative process describedabove. In the variation, the values of the early and late correlationsare independently examined to estimate the location of the precisecorrelation peak. This offset is directly taken as the PN code phasemeasurement without performing additional correlations. This methodsaves computation, but will be less accurate in the presence of noise.

Another variation of the method 600 is to perform the early-latecorrelation on the presummed groups of samples as formed in step 612.The advantage of this approach is that it reduces the number ofoperations required to perform the accumulation since the presummingreduces the size of the data. It should be noted that in thisformulation the code NCO operation would have to be modified toperiodically jump forward to account for the delay between thepre-summed blocks.

Also, it should be noted that the early-late correlator is one of manycorrelation forms that can be used. The approach is very general and canbe used to formulate a variety of correlation impulse responses.

For example, a combination of four delayed reference waveforms can beused to form a correlator with the desirable property of limitingresponse of the correlator to a very small window around the truecorrelation peak. This technique helps eliminate corruption of the delayestimate by multipath delayed signals. This, and the other techniquesused in conventional tracking receivers to reject code multipath areapplicable. Moreover, many of these correlation forms also provideestimates of the amount of multipath present. For example, in thepresence of multipath, a narrow correlator spacing will yield differentresults from a standard E-L spacing. These differences can provide anestimate of the multipath in the system. Furthermore, such metrics couldbe sent to the position server to improve the position solution orprovide warnings when accuracy's are degraded.

FIG. 7 depicts a flow diagram of a method 700 for peak identificationfor locating a mobile device. This method corresponds to the peakidentification process of step 650 of FIG. 6. The peak identificationmethod 700 begins by examining the list of candidate peaks resultingfrom peak detection step 640 in FIG. 6. This list contains the location(delay offset), PN, and magnitude for each peak. The peaks found in eachconvolution are a result of correlation and correlation sidelobesbetween the desired satellite signal, as well as cross correlationcomponents from other satellites. Because of the possible high dynamicrange between satellites, it cannot be assumed that the largest peakfound in each convolution is a result of correlation against the desiredsignal. However, the fact that the cross correlation properties of thePN codes are known in advance can be used to eliminate false peaks. Onesuch algorithm for eliminating false peaks is as follows. The method 700begins at step 710 and proceeds to step 720 where all peaks are searchedand the largest peak selected. This largest peak will always correspondto a true correlation peak. In step 740, method 700 determines thelargest peak and proceeds to step 760.

In step 760, all the sidelobes and cross correlation peaks associatedwith the true correlation peak are eliminated from the list of peaksbased on the known code sidelobe and cross correlation properties. Instep 770, the remaining peaks are searched for the largest remainingpeak. Since sidelobes and cross correlations from the first signal havebeen removed, this peak must also be a true correlation. The sidelobesand cross correlation of this second peak is eliminated, and in step780, the method 700 continues until all true correlation peaks have beenidentified. In step 785, a list of all the true peaks is obtained, and,in step 790, the method 700 ends.

Hardware Embodiment of the Parallel GPS Correlator

In alternative embodiment for signal processing, the parallel GPScorrelator is implemented via custom digital logic hardware contained inan application specific integrated circuit (ASIC) referred to as theBlock Search ASIC. Other components of the ASIC include a microprocessorcore, program and data memory, and a dual port memory used by the customlogic and the microprocessor. Unlike the software embodiment of theparallel GPS correlator, the hardware implementation processes incomingIF data samples in real time and therefore does not need a large samplememory as required in step 610 of FIG. 6. Furthermore, the hardwareembodiment, unlike the software embodiment, requires minimalcomputational power in the host CPU. The preferred embodiment, hardwareor software, for a particular device will depend strongly on theresources (i.e. memory and CPU) that are available from other functionsin the mobile device.

FIG. 8 depicts a functional block diagram of this aforementioned secondembodiment of a parallel GPS correlator element 800 within a mobiledevice. The element 800 comprises a plurality of parallel correlatorchannels 802 _(i), where i is an integer. The correlator channels 802are substantially identical to one another; therefore, the details ofcorrelator channels 802 _(l) are described with respect to FIG. 8. Inparticular, IF input samples 801 are first multiplied using multiplier805 by a complex exponential term to remove an IF carrier frequency. Thecomplex exponential is generated by numerically controlled oscillator812. The NCO frequency is set to the IF frequency, which is generallycomposed of a fixed term (due to the design of the RF front end), and acarrier frequency correction term as provided by the signal search step406 or 412. The multiplication step generates a complex result, i.e.,the result is composed of an in-phase term (generated by mixing with acosine function of the carrier phase) and a quadrature term (generatedby mixing with a sine function of the carrier phase). In FIG. 8, forclarity, the flow of complex values with in-phase and quadraturecomponents are represented by double-lined arrows.

The carrier-corrected samples are resampled using resampler 803 in orderto yield samples at the desired input rate for the correlation process.In one embodiment, the resampler 803 is implemented as an integrate anddump circuit which periodically provides a pre-summed value to theparallel correlator 815. The dump event of the resampler is controlledby a second NCO 813 that generates a sample signal that properlydistributes the chips of the incoming PN modulation across the parallelcorrelator 815. The NCO value is programmed based on the expectedpseudo-range rate of the incoming signal.

It should be noted that the digital circuit runs on a single clock, suchthat the time interval of an individual pre-sum in resampler 803 willalways begin and end on a clock cycle. On an instantaneous basis, thiswill introduce variations in the sample timing relative to the incomingsignal. These variations, however, cause only slight changes in theoverall correlation process because the NCO will, on average, generatethe correct sampling timing.

The outputs of the resampler 803 pass to correlator 815 which performthe task of calculating the convolution between the received signal anda set of reference waveforms for each satellite in view. Each channel802 contains a plurality of delay units 807 and a large multiply-and-addlogic block 804 that computes the correlation between a full epoch ofinput data 801 and the complete PN code sequence for the desiredsatellite. On each clock cycle, a new correlation result for aparticular delay value is generated and stored in random access memory(RAM) 810. After a full epoch of clock cycles, the RAM 810 contains acomplete set of correlation results for all delays. This array ofresults is the convolution between the input signal 801 and thereference waveform produced by a PN code generator 814.

In one embodiment, eight parallel correlator channels 802 are used,allowing simultaneous sensing of up to eight satellites in view. Thesize of each correlator 815 within each channel depends on thegranularity required in the convolution result. A 2046-wide parallelcorrelator provides convolution results spaced at intervals of one-halfof a PN code chip. This is adequate to detect and estimate the locationof the true peak correlation, which will, in general fall between binson the convolution.

The block search hardware 800 is designed to detect and measureextremely weak signals. Due to noise, interference, and crosscorrelation effects these low signal levels are not detectable throughanalyzing a single epoch of data. To enhance sensitivity, the blocksearch hardware 800 integrates the results from hundreds of individualconvolutions to generate a single composite convolution with improvedsignal to noise characteristics. Two types of averaging are performed:coherent averaging in coherent accumulator 806 and non-coherentaveraging in non-coherent accumulator 808. The motivation for using acombination of coherent and non-coherent averaging is substantially thesame as was described in steps 660 and 670 of method 600 in FIG. 6.

Coherent averaging is implemented by directly summing the results ofmultiple convolutions and using the RAM 810 to store intermediateresults. As each correlation is computed, the result is added to anongoing summation in the RAM 810 for that delay value. At the conclusionof the coherent averaging interval the RAM 810 holds a compositeconvolution result. One embodiment uses a nine epoch coherent averagingperiod (an epoch meaning a full cycle of the PN code). To further extendthe averaging time, non-coherent averaging is used. Non-coherentaveraging consists of summing the complex magnitudes of the individualconvolution results to yield a composite result with improved signal tonoise characteristics. The non-coherent averaging process builds uponthe results of coherent averaging. As each coherent averaging intervalends, the resulting coherent average is magnitude-squared summed with anongoing non-coherent averaging value stored in RAM 810. This processruns for the desired total averaging interval, for example one second.

Before processing begins, each parallel correlator must be pre-loadedwith the reference waveform. There are many possible ways to achievethis preloading. The waveforms for all 32 PN codes, for example, couldbe stored in hardware and selected via a multiplexer. Alternatively, thereference waveform could be stored in microprocessor ROM and loaded intothe hardware at run time. In a preferred embodiment, the referencewaveform is generated by PN code generator 814. During initialization ofthe correlator 815, this reference waveform is clocked in using delayunits 809. A single PN code generator 814 can be used to load all eightcorrelators in sequence.

The Block Search hardware 800 includes a simple microprocessor, runninga software program stored in memory. The software works in conjunctionwith the parallel GPS correlator to complete the GPS processingfunctions in the mobile device. One key responsibility of the softwareincludes performing all initialization functions beginning with receiptof the initialization packet from the position server through toprogramming all necessary hardware elements such as NCO's and PN codegenerators. Another responsibility is managing the coherent/non coherentintegration process through appropriate control of hardware interfaces,as well as implementing the peak identification process. The algorithmsused for the latter purpose are substantially the same as thosedescribed in the Software Processing Algorithm description andillustrated in FIG. 7. Furthermore, the software includes implementing apeak measurement algorithm to precisely estimate the actual signalmeasurements based on the averaged correlation results accumulated inthe on-chip RAM. The estimation process will use aninterpolation/filtering algorithm that makes an estimate of the truepeak location from nearby correlation results. Moreover, the softwareprovides a communication protocol, such as a serial bus, to communicatewith the host device.

The Block Search implementation described is one particular embodiment.As with many hardware signal processing systems, a broad array ofhardware implementations are possible.

PALS Applications

For cellular phones to be location enabled, PALS technology isintegrated into the circuit board and operating systems of the mobiledevice. The Software Approach described above requires integration ofadditional ROM/RAM memory, a GPS RF receiver and operating systemmodifications to the integral DSPs. The hardware approach described inFIG. 8, does not leverage the DSPs and insures no loss or interruptionof voice processing capability during position related processing. Thissolution requires the installation of the PALS ASIC chip in lieu ofleveraging the DSP.

For a 1.X way pager to be location enabled, (1.0 way pagers can belocated via an autonomous/DGPS assist method only due to receive onlyoperation), PALS technology is integrated into the hardware andoperating protocols of the mobile device. Pagers do not have thepowerful voice processing DSPs of cell phones and thus require ahardware solution similar to that described above. This includesintegration of additional ROM/RAM memory, a GPS RF receiver and the PALSASIC chip. The GPS receiver front end shares an antenna with the pagertransceiver as well the power supply.

Wireless Internet devices include personal digital assistants, lap toppersonal computers, and hand held personal computers. These devicesoriginally designed as personal information managers (PIM's) areevolving into palm/hand size mobile PCs and integrate PIM functions,word processing, spreadsheet, Internet browser and a wireless modem.Recent alignments within the wireless industry point to an eventualintegration of voice and data creating an entirely new family of mobiletelephony devices. Accurate device position is a valuable parameter forfiltering location specific search results, providing real-timedirections or locating people and assets. The Air-IP-Interface willeasily support the half-duplex data transmission required by the PALSlocation solution. Assuming the device is delivered with a wirelessmodem, the PALS technology must by integrated into the hardware andoperating system software. PCs have no DSPs and thus require an enhancedhardware solution similar to that described above. This includesintegration of additional ROM/RAM memory, a GPS RF receiver and the PALSASIC chip. The GPS front end will share the device power supply as wellas the transceiver's antenna.

Another implementation of the wireless client/server based locationdevice uses a single function, position device that acts solely as alocation beacon and/or panic signal. No power hungry display or backlight is provided. The device consists of a GPS RF receiver, pagertransceiver, pager ASIC, PALS ASIC, RAM/ROM memory, power supply andantenna. This device will be locatable through a web portal or via standalone applets. The express purpose of this class of location enableddevice is person or asset tracking. No voice or text data communicationis included. Such a device would be suited for (stolen) vehicle oremployee tracking on a global scale.

Dialing 911 from most US based land-line telephones results in the callbeing switched (directed) to the predetermined Public Safety AnsweringPoint (PSAP). A public safety answering point is manned by operatorstrained in dispatching emergency services. The existing wirelineinfrastructure includes the callers identification as well as locationto assist the PSAP in deploying the necessary response. To date, thesame emergency 911 call placed on a wireless device will not be routedto the appropriate PSAP.

By imbedding a precisely calculated position along with the cellularphone owner's identification the PALS system will assist in routing 911calls to the appropriate PSAP. A 911 call placed by a wireless devicewill then render the same response as that of the land line variety. TheLocation Routing technology will be transparent to the caller andrequire no additional keystrokes.

Regardless of the nature of the location request (Cellular 911, Internetvia 2-way pager or PDA based location specific query) the client/serverposition solution relies on a series of timely data transactions. Oncequeried the Position Server sends a packet of information to the client.This information is known as the PALS initialization packet or PIP, thisdata is processed by the PALS block search ASIC or PALS software DSPbased technology to yield Satellite PNCode Phases. These results aretransmitted back to the server for accurate position calculation. Finalposition is then relayed to the querying party. The PIP contains thefollowing data such as approximate location (300 mile radius) determinedthrough a variety of means, known location of the RECEIVING basestation, cellular/pager home area, previous solution assumption, limitedsubscription area or direct entry via keyboard (PDA/HHPC); timing datanecessary to offset mobile device local clock to true GPS time andsatellite orbit models/atmospheric corrections determined by thePosition Server GPS network.

There are seven discrete transactions associated with locating a mobiledevice in the Enhanced 911 model. FIG. 10 depicts a functional blockdiagram of a system 1000 for transferring data in an emergency911/cellular phone environment. Each step represents data exchange only,voice transactions are not detailed. A personal asset location system(PALS) request is initiated via a wireless carrier based on anapproximate position of the cellular phone. Using path 1004, thecentralized server 120 transmits the PALS initialization packet (PIP)reply back to the wireless carrier 1001 and the PIP is relayed throughpath 1006 to the cellular phone 1020. The cellular phone 1020 transfersthe satellite PN code Phases from visible GPS satellites through path1008 to the wireless carrier 1001. The wireless carrier 1001 forwardsvia link 1002 the satellite PN code phases to the centralized server120.

Cell phones transceive voice and data differently based on the AirInterface. Analog phones use a method referred to as AMPS (AdvancedMobile Phone Service). Digital and PCS phones use varying technologiesfrom TDMA to CDMA and combinations of all three. These are industrywide, open standards and therefore can be enhanced or modified byconsortium agreement. In all cases a portion of the available bandwidthis compromised by the protocol overhead (this is the non-voice data usedto identify the caller, control the power output, select transceiverchannels, handoff calls cell to cell, etc.). This non-voice bandwidthwill support the small packets of data required to determine thelocation of the mobile device.

The wireless industry is moving towards integrating an Air-IP-Interface.This will allow much more non-voice bandwidth for data such as the PIPor Geo-Coded location replies. PDAs (personal digital assistants) andHHPCs (hand-held personal computers) already allow Internet access viaanalog modem. Such a system will support GPS client-server datatransactions immediately with no Air Interface modifications.

Another valuable function of the PALS E911 Location Solution is thevirtual routing of these calls to the appropriate public safetyanswering point (PSAP) 1016. The centralized server 120 computes thecellular phone position from the satellite PN code Phases and transmitsthe cellular phone location back to the wireless carrier. Thus, thecentralized server 120 returns a final position and in turn calculatesthe nearest or most appropriate PSAP 1016 from a known nationaldatabase. Once relayed to the carrier this information allows thewireless call to be switched and connected to the PSAP for disposition.The wireless carrier, through link 1014, forwards the cellular phonelocation to a PSAP via a public switch telephone network (PSTN) 1022.The real time nature of these transactions makes the PALS E911 LocationSolution transparent to the caller.

The PALS system leverages Internet technology to provide a ubiquitousmedia for hosting person and asset location services. The web site isthe portal into web-based asset and people tracking services targeted tobusiness and consumers. Through a standard Internet browser, customersare provided with simple user interfaces that allow them to locate theirassets or loved ones equipped with PALS-enabled devices identified byuser codes. The web site can also be programmed to provide trackingservices for groups of assets.

For example, a business could program the site to locate and display thepositions of an entire fleet of vehicles. Advanced features, such asscheduled reporting or alarms could also be incorporated into the website. The use of the Internet for hosting position services contrastssharply with today's proprietary tracking software systems. Thecombination of an Internet interface and a client-server positioningarchitecture provides great advances in performance and ease-of-use.

FIG. 11 depicts a functional block diagram of a system 1100 fortransferring location data in a pager environment. There are tendiscrete transactions associated with locating a mobile device in theInternet based pinging model. Each step represents data exchange onlysince voice is not transmitted. The nature of each node to nodeinterface is indicated by dotted lines. This model stipulates thelocation query be generated from a remote user connected to theInternet. A query is generated at the web portal 1120 or relayed throughthe web portal by a PC based stand alone application. In either case aGUI (graphic user interface) will prompt the user for an identifying PINof the mobile device 1130. The results of the location query (or ping)will be a geo-coded position displayed on a scale map with pertinentcross streets and landmarks. The mobile device may or may not prompt thewearer to the pinging process.

Specifically, in response to a query sent via path 1102, the wirelesspaging carrier responds (path 1104) over the Internet 1122 with anapproximate position and the Internet routes the PALS initializationpacket (PIP) based on the known approximate location to the centralizedserver 120. The centralized server 120 replies to the PIP request viapath 1108 and the request is sent via the Internet to the wirelesscarrier along path 1110. The wireless carrier 1101 receives the requestfor an outgoing page containing the PALS initialization packet and,through path 1112, the wireless carrier forwards the page containing thePIP to the pager. The pager replies through path 1114 back to thewireless carrier with the satellite PN code Phases for the GPSsatellites that are visible to the pager. The wireless carrier relaysthrough path 1116 the satellite PN code phases to the web site, and theweb portal forwards through path 1118 the satellite pn code phases tothe centralized server 120. The centralized server computes the locationof the pager and forwards the information through path 1119 to the webportal 1120 for geo-coding.

The pagers transceive data differently based on the paging protocol.These are manufacturer specific standards and therefore are less likelyto be modified for application specific reasons. Unlike cellular airinterfaces, paging protocols are half duplex architecture and supportdata transmission in lieu of more bandwidth demanding voicecommunication. The data specific nature of 2-way pagers combined withthe low power requirement makes them an ideal wireless locationplatform. A small footprint and tiny integral antenna allow the deviceto worn by a person or implanted in an asset without impeding normalfunction. Two way paging communications may involve some latency of datadue to the wide geographic footprint, satellite up and down links andmessage queuing. The PALS location solution (as described previously)will not lose accuracy due to normal queuing delays.

As described above the pinging visitor to the PALS location portal website will be prompted to enter the mobile clients PIN (PersonalIdentification Number). The results of the query will be displayed in anumber of user configurable formats. Some outputs will necessitatemultiple pings and additional processing time. Few consumers can benefitfrom position information displayed as numerical latitude and longitude.The position information becomes valuable when presented in context. Forexample, latitude, longitude and altitude for course plotting orintegration to client mapping software; Geo-coded (variable scale) localmap showing cross street/landmark; geo-coded (variable scale) USGStopographical map showing location relative to native geography;positions rendered on 3-D virtual images of cities or other features;nearest street address, city, state and zip code; and routing directionsto located destination; optional speed and heading parameters.

For more demanding asset tracking uses a configurable stand-aloneapplication is provided. The applet facilitates tracking multipleclients (people or assets) at variable intervals. Client ID and positiondata may be displayed and updated (in previously defined output formats)in real-time or archived for post processing. The applet will access thePosition Server via the internet and the PALS Web Portal or thru thedownloadable applet and a dedicated dial-up wireline connection.

Wireless Internet Devices include Personal Digital Assistants, HandHelp/palm based Personal Computers and Lap Top Personal Computers. Thesewireless browsers allow mobile Internet connectivity via traditionalmodem/cellular protocol interface or through emerging wireless IP airinterfaces. The implications of enabling such devices with the PALSlocation solution are outlined above. The software running on thesedevices can either take the form of a generalized browser, which relieson the wireless Application Service Provider for application specificuser interactions, or a special purpose dedicated application running onthe wireless mobile device.

There are thirteen discrete transactions associated with the client(PDA, HHPC, LTPC) initiated location query. FIG. 12 depicts a functionalblock diagram of a system 1200 for transferring location data in awireless browser environment. Each path represents data exchange onlysince voice is not transmitted. The nature of each node to nodeinterface is indicated by dotted lines.

This model stipulates the location query be generated from the client(mobile device) connected by a wireless protocol to the Internet. Thequery may be generated via the mobile browser web portal or relayed thruthe web portal by the PDA resident applet. In either case, a GUI(graphic user interface) prompts the user for an identifying PIN of themobile device. The results of the location query (PING) is a geo-codedposition displayed on a scale map with pertinent cross streets andlandmarks.

The system 1200 begins with a position query on path 1202 from a PDAapplet thru the wireless carrier 1001. The carrier sends via path 1204the position query over the Internet 1230 to the web portal 1240 wherethe web site authorizes and requests via path 1206 a PALS initializationpacket (PIP) from the centralized server 120. The centralized server 122sends via path 1208 a PIP reply over the Internet to the web portalwhere the PIP reply to the applet is forwarded via path 1210 to thecarrier 1201. The carrier sends via path 1212 the PIP reply to theapplet to the PDA, and the PDA sends via path 1214 the visible GPSsatellite PN code phases to the position server via the wirelesscarrier. The satellite PN code phases are sent via path 1216 across theInternet 1230 and are routed to the centralized server via path 1218.The centralized server sends via path 1220 the final raw positions ofthe PDA across the Internet to the web portal 1240 where the rawposition with additional location specific data is sent via path 1222 tothe carrier. The position with additional location specific data is sentvia path 1224 to the browser applet in the PDA 1203.

A PALS Location Solution enabled Wireless Internet Device permits webbased queries using the mobile devices real-time location as a searchfilter that insures search results will reflect only those lying withina specified radius of the mobile device. This will allow locallypertinent data (addresses or telephone numbers), maps, landmarks, placesof business or current-position sensitive directions to be viewed viathe mobile browser.

While the foregoing is directed to the preferred embodiment of thepresent invention, other and further embodiments of the invention may bedevised without departing from the basic scope thereof, and the scopethereof is determined by the claims that follow.

1. A method of forming a pseudo-range model, comprising: receivingsatellite position information for a plurality of satellites; receivingan approximate position of a mobile device; computing at least one setof ranges from the mobile device to the plurality of satellites usingthe approximate position and the satellite position information; andforming a pseudo-range model from the at least one set of ranges, thepseudo-range model having pseudorange information and pseudorange rateinformation.
 2. The method of claim 1, further comprising: adjusting theat least one set of ranges to account for at least one of satelliteclock errors and ionosphere/troposphere errors.
 3. The method of claim1, wherein the step of computing at least one set of ranges comprises:computing respective sets of ranges at a plurality of different times.4. The method of claim 3, wherein the step of forming a pseudo-rangemodel comprises: determining a polynomial fit to the sets of ranges toform the pseudo-range model.
 5. The method of claim 4, wherein thepseudo-range model comprises:a _(i) +b _(i) dt+c _(i) dt ², where a_(i) corresponds to pseudorangeinformation to the ith satellite, and b_(i) and c_(i) correspond topseudo-range rate information for the ith satellite.
 6. The method ofclaim 1, wherein the satellite position information is derived from awide area model.
 7. The method of claim 1, wherein the pseudo-rangemodel is formed at a server in wireless communication with the mobiledevice.
 8. The method of claim 7, wherein the approximate position ofthe mobile device is determined using a wireless communication system incommunication with the server.
 9. A system for forming a pseudo-rangemodel, comprising; a mobile device for receiving signals from aplurality of satellites; and a server being in wireless communicationwith the mobile device; where the server is configured to compute atleast one set of ranges from the mobile device to the plurality ofsatellites using an approximate location of the mobile device andsatellite position information; and where the server is configured toform a pseudo-range model from the at least one set of ranges, thepseudo-range model having pseudorange information and pseudorange rateinformation.
 10. The system of claim 9, wherein the server is furtherconfigured to adjust the at least one set of ranges to account for atleast one of satellite clock errors and ionosphere/troposphere errors.11. The system of claim 9, wherein the server is further configured tocompute respective sets of ranges at a plurality of different times anddetermine a polynomial fit to the sets of ranges to form thepseudo-range model.
 12. The system of claim 11, wherein the pseudo-rangemodel comprises:a _(i) +b _(i) dt+c _(i) dt ², where a_(i) corresponds to pseudorangeinformation to the ith satellite, and b_(i) and c_(i) correspond topseudo-range rate information for the ith satellite.
 13. An apparatusfor forming a pseudo-range model, comprising: means for receivingsatellite position information for a plurality of satellites; means forreceiving an approximate position of a mobile device; means forcomputing at least one set of ranges from the mobile device to theplurality of satellites using the approximate position and the satelliteposition information; and means for forming a pseudo-range model fromthe at least one set of ranges, the pseudo-range model havingpseudorange information and pseudorange rate information.
 14. Theapparatus of claim 13, further comprising: means for adjusting the atleast one set of ranges to account for at least one of satellite clockerrors and ionosphere/troposphere errors.
 15. The apparatus of claim 13,wherein the means for computing at least one set of ranges comprises:means for computing respective sets of ranges at a plurality ofdifferent times.
 16. The apparatus of claim 15, wherein the means forforming a pseudo-range model comprises: means for determining apolynomial fit to the sets of ranges to form the pseudo-range model. 17.The apparatus of claim 16, wherein the pseudo-range model comprises:a _(i) +b _(i) dt+c _(i) dt ², where a_(i) corresponds to pseudorangeinformation to the ith satellite, and b_(i) and c_(i), correspond topseudo-range rate information for the ith satellite.
 18. The apparatusof claim 13, wherein the satellite position information is derived froma wide area model.
 19. The apparatus of claim 13, wherein thepseudo-range model is formed at a server in wireless communication withthe mobile device.
 20. The apparatus of claim 19, wherein theapproximate position of the mobile device is determined using a wirelesscommunication system in communication with the server.